A computational method for drug repositioning using publicly available gene expression data.

MOTIVATION: The identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. The advent of high throughput microarray technology has enabled comprehensive monitoring of transcriptional response associated with various disease states and drug treatments. This data can be used to characterize disease and drug effects and thereby give a measure of the association between a given drug and a disease. Several computational methods have been proposed in the literature that make use of publicly available transcriptional data to reposition drugs against diseases. METHOD: In this work, we carry out a data mining process using publicly available gene expression data sets associated with a few diseases and drugs, to identify the existing drugs that can be used to treat genes causing lung cancer and breast cancer. RESULTS: Three strong candidates for repurposing have been identified- Letrozole and GDC-0941 against lung cancer, and Ribavirin against breast cancer. Letrozole and GDC-0941 are drugs currently used in breast cancer treatment and Ribavirin is used in the treatment of Hepatitis C.

Neuropsychological Assessment of Older Adults in Nepal for Population-Based Dementia Ascertainment: Needs, Challenges, and Opportunities.

The population of Nepal is rapidly aging, as in other low and middle-income countries, and the number of individuals living with Alzheimer's Disease and related dementias (ADRD) is expected to increase. However, information about the neuropsychological assessment of ADRD in Nepal is lacking. We first aimed to examine the needs, challenges, and opportunities associated with the neuropsychological assessment of older adults in Nepal for population-based ADRD ascertainment. Second, we introduce the Chitwan Valley Family Study-Study of Cognition and Aging in Nepal (CVFS-SCAN), which is poised to address these needs, and its collaboration with the Harmonized Cognitive Assessment Protocol (HCAP) international network. We reviewed the existing literature on the prevalence, risk factors, available neuropsychological assessment instruments, and sociocultural factors that may influence the neuropsychological assessment of older adults for ADRD ascertainment in Nepal. Our review revealed no existing population-based data on the prevalence of ADRD in Nepal. Very few studies have utilized formal cognitive assessment instruments for ADRD assessment, and there have been no comprehensive neuropsychological assessment instruments that have been validated for the assessment of ADRD in Nepal. We describe how the CVFS-SCAN study will address this need through careful adaptation of the HCAP instrument. We conclude that the development of culturally appropriate neuropsychological assessment instruments is urgently needed for the population-based assessment of ADRD in Nepal. The CVFS-SCAN is designed to address this need and will contribute to the growth of global and equitable neuropsychology and to the science of ADRD in low- and middle-income countries.

A systems biology approach to the global analysis of transcription factors in colorectal cancer.

BACKGROUND: Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity's own significance but also uncover relationships with novel biological entities.To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC). METHODS: We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC. RESULTS: The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways. CONCLUSIONS: Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.

Utilization of electronic health records for the assessment of adiponectin receptor autoantibodies during the progression of cardio-metabolic comorbidities.

BACKGROUND: Diabetes is a complex, multi-symptomatic disease whose complications drives increases in healthcare costs as the diabetes prevalence grows rapidly world-wide. Real-world electronic health records (EHRs) coupled with patient biospecimens, biological understanding, and technologies can characterize emerging diagnostic autoimmune markers resulting from proteomic discoveries. METHODS: Circulating autoantibodies for C-terminal fragments of adiponectin receptor 1 (IgG-CTF) were measured by immunoassay to establish the reference range using midpoint samples from 1862 participants in a 20-year observational study of type 2 diabetes and cardiovascular arterial disease (CVAD) conducted by the Fairbanks Institute. The White Blood Cell elastase activity in these patients was assessed using immunoassays for Bikunin and Uristatin. Participants were assigned to four cohorts (healthy, T2D, CV, CV+T2D) based on analysis of their EHRs and the diagnostic biomarkers values and patient status were assessed ten-years post-sample. RESULTS: The IgG-CTF reference range was determined to be 75-821 ng/mL and IgG-CTF out-of-range values did not predict cohort or comorbidity as determined from the EHRs at 10 years after sample collection nor did IgG-CTF demonstrate a significant risk for comorbidity or death. Many patients at sample collection time had other conditions (hypertension, hyperlipidemia, or other risk factors) of which only hypertension, Uristatin and Bikunin values correlated with increased risk of developing additional comorbidities (odds ratio 2.58-13.11, P<0.05). CONCLUSIONS: This study confirms that retrospective analysis of biorepositories coupled with EHRs can establish reference ranges for novel autoimmune diagnostic markers and provide insights into prediction of specific health outcomes and correlations to other markers.

Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum.

BACKGROUND: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. OBJECTIVE: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. METHODS: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. RESULTS: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics-based on the Akaike information criterion values ranging from -642.75 to -412.32-were statistically significant. CONCLUSIONS: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life.

A system biology approach for understanding the miRNA regulatory network in colon rectal cancer.

In this paper we present a systems biology approach to the understanding of the miRNA-regulatory network in colon rectal cancer. An initial set of significant genes in Colon Rectal Cancer (CRC) were obtained by mining relevant literature. An initial set of cancer-related miRNAs were obtained from three databases: miRBase, miRWalk, Targetscan and GEO microarray experiment. First principle methods were then used to generate the global miRNA-gene network. Significant miRNAs and associated transcription factors in the global miRNA-gene network were identified using topological and sub-graph analyses. Eleven novel miRNAs were identified and three of the novel miRNAs, hsa-miR-630, hsa-miR-100 and hsa-miR-99a, were further analysed to elucidate their role in CRC. The proposed methodology effectively made use of literature data and was able to show novel, significant miRNA-transcription associations in CRC.

Systems biology approach to stage-wise characterization of epigenetic genes in lung adenocarcinoma.

BACKGROUND: Epigenetics refers to the reversible functional modifications of the genome that do not correlate to changes in the DNA sequence. The aim of this study is to understand DNA methylation patterns across different stages of lung adenocarcinoma (LUAD). RESULTS: Our study identified 72, 93 and 170 significant DNA methylated genes in Stages I, II and III respectively. A set of common 34 significant DNA methylated genes located in the promoter section of the true CpG islands were found across stages, and these were: HOX genes, FOXG1, GRIK3, HAND2, PRKCB, etc. Of the total significant DNA methylated genes, 65 correlated with transcription function. The epigenetic analysis identified the following novel genes across all stages: PTGDR, TLX3, and POU4F2. The stage-wise analysis observed the appearance of NEUROG1 gene in Stage I and its re-appearance in Stage III. The analysis showed similar epigenetic pattern across Stage I and Stage III. Pathway analysis revealed important signaling and metabolic pathways of LUAD to correlate with epigenetics. Epigenetic subnetwork analysis identified a set of seven conserved genes across all stages: UBC, KRAS, PIK3CA, PIK3R3, RAF1, BRAF, and RAP1A. A detailed literature analysis elucidated epigenetic genes like FOXG1, HLA-G, and NKX6-2 to be known as prognostic targets. CONCLUSION: Integrating epigenetic information for genes with expression data can be useful for comprehending in-depth disease mechanism and for the ultimate goal of better target identification.

Cliques for the identification of gene signatures for colorectal cancer across population.

BACKGROUND: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Studies have correlated risk of CRC development with dietary habits and environmental conditions. Gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. Such processes can be used to evaluate potential drug targets. Though recognition of CRC gene-signatures across populations is crucial to better understanding potential novel treatment options for CRC, it remains a challenging task. RESULTS: We developed a topological and biological feature-based network approach for identifying the gene signatures across populations. In this work, we propose a novel approach of using cliques to understand the variability within population. Cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. Our study was based on four publicly available expression datasets belonging to four different populations across the world. We identified cliques of various sizes (0 to 7) across the four population networks. Cliques of size seven were further analyzed across populations for their commonality and uniqueness. Forty-nine common cliques of size seven were identified. These cliques were further analyzed based on their connectivity profiles. We found associations between the cliques and their connectivity profiles across networks. With these clique connectivity profiles (CCPs), we were able to identify the divergence among the populations, important biological processes (cell cycle, signal transduction, and cell differentiation), and related gene pathways. Therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. In this work we demonstrate the power and effectiveness of cliques to study CRC across populations. CONCLUSIONS: We developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in CRC gene profiles across four populations with unique dietary habits.

A micro-level event-centered approach to investigating armed conflict and population responses.

In this article, we construct and test a micro-level event-centered approach to the study of armed conflict and behavioral responses in the general population. Event-centered approaches have been successfully used in the macro-political study of armed conflict but have not yet been adopted in micro-behavioral studies. The micro-level event-centered approach that we advocate here includes decomposition of a conflict into discrete political and violent events, examination of the mechanisms through which they affect behavior, and consideration of differential risks within the population. We focus on two mechanisms: instability and threat of harm. We test this approach empirically in the context of the recent decade-long armed conflict in Nepal, using detailed measurements of conflict-related events and a longitudinal study of first migration, first marriage, and first contraceptive use. Results demonstrate that different conflict-related events independently shaped migration, marriage, and childbearing and that they can simultaneously influence behaviors in opposing directions. We find that violent events increased migration, but political events slowed migration. Both violent and political events increased marriage and contraceptive use net of migration. Overall, this micro-level event-centered approach yields a significant advance for the study of how armed conflict affects civilian behavioral responses.

Computational studies of chiral catalysts: a comparative molecular field analysis of an asymmetric Diels-Alder reaction with catalysts containing bisoxazoline or phosphinooxazoline ligands.

A QSAR using Comparative Molecular Field Analysis (CoMFA) is developed for a set of 23 catalysts containing bisoxazoline or phosphinooxazoline ligands that are known to induce asymmetry during the Diels-Alder reaction of N-2-alkenoyl-1,3-oxazolidine-2-one with cyclopentadiene. It is shown that extremely high q(2) statistics can be derived by using standard modeling protocols when internal validation alone is done as well as when an external test set is used. From these models it is shown that approximately 70% of the variance in the observed enantiomeric excess can be attributed to the steric field and the remainder of the variance to the electrostatic field. Suggestions about how to improve the performance of inefficient catalysts are given.